{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3171a0f1-eaeb-4da0-a1d2-bd17bd02064f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "library(SeuratDisk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b6890880-8888-4844-aba6-a3b4bf9ef490",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘5.3.0’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "packageVersion(\"Seurat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e6a8cbd8-0932-4c36-b1fe-f0c27d82f45f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "library(Seurat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4cc5ff65-1f26-4445-881e-b76d68ff49c7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "library(dplyr)\n",
    "library(Seurat)\n",
    "library(patchwork)\n",
    "library(readr)\n",
    "library(RColorBrewer)\n",
    "library(ggplot2)\n",
    "library(VisCello)\n",
    "library(viridis)\n",
    "library(forcats)\n",
    "library(cowplot)\n",
    "library(GSEABase)\n",
    "library(ComplexHeatmap)\n",
    "library(DoubletFinder)\n",
    "library(irlba)\n",
    "library(AUCell) \n",
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5149dc0a-4499-4af9-a7a8-f41725c4ece0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘2.0.6’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(DoubletFinder)\n",
    "packageVersion(\"DoubletFinder\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d93c075d-3d35-475b-adbc-1e4522bc6110",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "library(ggplot2)\n",
    "library(AUCell) # x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8aa73cd-ae8d-47c5-99da-cfc35257585f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_dir_MACS = \"../data/geo/MACS/\"  # 替换为实际路径\n",
    "data_dir_H21 = \"../data/geo/H21/\"\n",
    "data_dir_H14 = \"../data/geo/H14/\"\n",
    "data_dir_H41 = \"../data/geo/H41/\"\n",
    "data_dir_H39 = \"../data/geo/H39/\"\n",
    "data_dir_H38 = \"../data/geo/H38/\"\n",
    "data_dir_H36 = \"../data/geo/H36/\"\n",
    "data_dir_H35 = \"../data/geo/H35/\"\n",
    "data_dir_H34 = \"../data/geo/H34/\"\n",
    "data_dir_H33 = \"../data/geo/H33/\"\n",
    "data_dir_H32 = \"../data/geo/H32/\"\n",
    "data_dir_H24 = \"../data/geo/H24/\"\n",
    "data_dir_H23 = \"../data/geo/H23/\"\n",
    "\n",
    "MACS <- Read10X(data.dir = data_dir_MACS)\n",
    "H21 <- Read10X(data.dir = data_dir_H21)\n",
    "H23 <- Read10X(data.dir = data_dir_H23)\n",
    "H24 <- Read10X(data.dir = data_dir_H24)\n",
    "H32 <- Read10X(data.dir = data_dir_H32)\n",
    "H33 <- Read10X(data.dir = data_dir_H33)\n",
    "H34 <- Read10X(data.dir = data_dir_H34)\n",
    "H35 <- Read10X(data.dir = data_dir_H35)\n",
    "H36 <- Read10X(data.dir = data_dir_H36)\n",
    "H38 <- Read10X(data.dir = data_dir_H38)\n",
    "H39 <- Read10X(data.dir = data_dir_H39)\n",
    "H41 <- Read10X(data.dir = data_dir_H41)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2369bc5-ff68-494e-b17a-0a03d6d70bb3",
   "metadata": {},
   "source": [
    "# Initialize the Seurat object with the raw (non-normalized data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "781b7b49-3532-4efd-9faf-8fe998890b73",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "26337 features across 7672 samples within 1 assay \n",
       "Active assay: RNA (26337 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "26854 features across 8930 samples within 1 assay \n",
       "Active assay: RNA (26854 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "27155 features across 12401 samples within 1 assay \n",
       "Active assay: RNA (27155 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "26628 features across 9433 samples within 1 assay \n",
       "Active assay: RNA (26628 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "25632 features across 4853 samples within 1 assay \n",
       "Active assay: RNA (25632 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "25511 features across 8437 samples within 1 assay \n",
       "Active assay: RNA (25511 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "25452 features across 7189 samples within 1 assay \n",
       "Active assay: RNA (25452 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "26154 features across 8406 samples within 1 assay \n",
       "Active assay: RNA (26154 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "27311 features across 11200 samples within 1 assay \n",
       "Active assay: RNA (27311 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "26791 features across 8507 samples within 1 assay \n",
       "Active assay: RNA (26791 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "27092 features across 10242 samples within 1 assay \n",
       "Active assay: RNA (27092 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "27444 features across 9351 samples within 1 assay \n",
       "Active assay: RNA (27444 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MACS <- CreateSeuratObject(counts = MACS, project = \"H14_MACS\", min.cells = 3, min.features = 100)\n",
    "MACS\n",
    "H21 <- CreateSeuratObject(counts = H21, project = \"H21\", min.cells = 3, min.features = 100)\n",
    "H21\n",
    "H23 <- CreateSeuratObject(counts = H23, project = \"H23\", min.cells = 3, min.features = 100)\n",
    "H23\n",
    "H24 <- CreateSeuratObject(counts = H24, project = \"H24\", min.cells = 3, min.features = 100)\n",
    "H24\n",
    "H32 <- CreateSeuratObject(counts = H32, project = \"H32\", min.cells = 3, min.features = 100)\n",
    "H32\n",
    "H33 <- CreateSeuratObject(counts = H33, project = \"H33\", min.cells = 3, min.features = 100)\n",
    "H33\n",
    "H34 <- CreateSeuratObject(counts = H34, project = \"H34\", min.cells = 3, min.features = 100)\n",
    "H34\n",
    "H35 <- CreateSeuratObject(counts = H35, project = \"H35\", min.cells = 3, min.features = 100)\n",
    "H35\n",
    "H36 <- CreateSeuratObject(counts = H36, project = \"H36\", min.cells = 3, min.features = 100)\n",
    "H36\n",
    "H38 <- CreateSeuratObject(counts = H38, project = \"H38\", min.cells = 3, min.features = 100)\n",
    "H38\n",
    "H39 <- CreateSeuratObject(counts = H39, project = \"H39\", min.cells = 3, min.features = 100)\n",
    "H39\n",
    "H41 <- CreateSeuratObject(counts = H41, project = \"H41\", min.cells = 3, min.features = 100)\n",
    "H41"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "076af142-12ef-49d0-b78b-6e2d984725fa",
   "metadata": {},
   "source": [
    "# Preprocess samples individually to remove doublets -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "33ed63fa-6d40-4cb9-8c1f-70d762ddcd1b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'doubletFinder'</li><li>'find.pK'</li><li>'modelHomotypic'</li><li>'paramSweep'</li><li>'pbmc_small'</li><li>'summarizeSweep'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'doubletFinder'\n",
       "\\item 'find.pK'\n",
       "\\item 'modelHomotypic'\n",
       "\\item 'paramSweep'\n",
       "\\item 'pbmc\\_small'\n",
       "\\item 'summarizeSweep'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'doubletFinder'\n",
       "2. 'find.pK'\n",
       "3. 'modelHomotypic'\n",
       "4. 'paramSweep'\n",
       "5. 'pbmc_small'\n",
       "6. 'summarizeSweep'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"doubletFinder\"  \"find.pK\"        \"modelHomotypic\" \"paramSweep\"    \n",
       "[5] \"pbmc_small\"     \"summarizeSweep\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## error \"paramSweep_v3\"   ls(\"package:DoubletFinder\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1a5d5239-85d8-4ac4-aa5d-1bc4ea53a6ee",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'doubletFinder'</li><li>'find.pK'</li><li>'modelHomotypic'</li><li>'paramSweep'</li><li>'pbmc_small'</li><li>'summarizeSweep'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'doubletFinder'\n",
       "\\item 'find.pK'\n",
       "\\item 'modelHomotypic'\n",
       "\\item 'paramSweep'\n",
       "\\item 'pbmc\\_small'\n",
       "\\item 'summarizeSweep'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'doubletFinder'\n",
       "2. 'find.pK'\n",
       "3. 'modelHomotypic'\n",
       "4. 'paramSweep'\n",
       "5. 'pbmc_small'\n",
       "6. 'summarizeSweep'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"doubletFinder\"  \"find.pK\"        \"modelHomotypic\" \"paramSweep\"    \n",
       "[5] \"pbmc_small\"     \"summarizeSweep\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls(\"package:DoubletFinder\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c579fa24-5025-4bcb-82ff-1f0c1d6ab95b",
   "metadata": {},
   "source": [
    "# 手动doublefind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b41a94a2-928c-40ad-9905-a64d2058f536",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "MACS[[\"percent.mt\"]] <- PercentageFeatureSet(MACS, pattern = \"^MT-\")\n",
    "MACS <- subset(MACS, subset = nFeature_RNA > 100 & nFeature_RNA < 10000 & percent.mt < 10)\n",
    "# ob.list[[i]] <- subset(ob.list[[i]], cells = cells.use)\n",
    "MACS <- NormalizeData(MACS)\n",
    "MACS <- FindVariableFeatures(MACS)\n",
    "MACS <- ScaleData(MACS)\n",
    "MACS <- RunPCA(MACS, features = VariableFeatures(object = ob.list[[i]]))\n",
    "MACS <- RunUMAP(MACS, dims = 1:30, reduction.name = \"UMAP_dim30\", reduction.key = \"UMAP_dim30_\")\n",
    "MACS <- RunUMAP(MACS, dims = 1:50, reduction.name = \"UMAP_dim50\", reduction.key = \"UMAP_dim50_\")\n",
    "MACS <- FindNeighbors(MACS, dims = 1:30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15c89acc-0619-4dae-9dab-ec68a296c6b3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "MACS <- FindClusters(MACS, resolution = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53306440-dfd9-4150-98ce-1910c5521a85",
   "metadata": {},
   "source": [
    "## 函数检查"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7a1c93c-d8a9-434e-91bc-a23cbd1d5789",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sweep.res.list <- paramSweep(MACS, PCs = 1:50, sct = FALSE) # 生成不同 pK 下的双胞模拟结果。  # 模拟不同邻域大小（pK）下的双胞检测效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dee349de-ff0d-4028-b272-8c4a42bc1022",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sweep.stats <- summarizeSweep(sweep.res.list, GT = FALSE)  # 汇总模拟统计量（如假阳性率）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42d26901-2e2c-4b4d-a34a-f48a6e2266b7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "bcmvn <- find.pK(sweep.stats) # 选择 BCmetric（双胞检测指标）峰值对应的 pK（默认 pK=0.09）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "821f7a6a-453d-4486-a48c-8594b4931246",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "best.pK <- as.numeric(as.character(bcmvn$pK[which.max(bcmvn$BCmetric)]))\n",
    "best.pK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc6f768a-f114-4150-b125-fe07723ae5e6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "colnames(MACS@meta.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96f35e73-ce3e-4d85-b105-b996568378e7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "annotations <- MACS@meta.data$seurat_clusters\n",
    "annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "396039e3-73ad-4988-84c0-2c6b364bc734",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "homotypic.prop <- modelHomotypic(annotations)\n",
    "homotypic.prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fe84e4b-ddcb-48f7-8952-aa04d959b3b7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "exp_rate = 0.02\n",
    "nExp_poi <- round(exp_rate * ncol(MACS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a8a7004-81cd-44a5-b9b6-ebb1a286ecfc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "nExp_poi.adj <- round(nExp_poi * (1 - homotypic.prop))\n",
    "nExp_poi.adj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b960e60f-e3a3-486c-b960-d2f6557ae8a0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "MACS[[\"RNA\"]]$data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f21db8fd-cf3c-464e-82e2-0faa6acfa598",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class(MACS)  # 应为 \"Seurat\"\n",
    "class(MACS@assays$RNA@data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47a631ca-f609-44c3-a3ca-ed7f46ba1936",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "MACS <- doubletFinder(\n",
    "MACS,\n",
    "PCs = 1:50,\n",
    "pN = 0.25,\n",
    "pK = best.pK,\n",
    "nExp = nExp_poi,\n",
    "# reuse.pANN = FALSE,\n",
    "sct = FALSE\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5782c3e-d6fc-46df-94d3-5a131cae5b3f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pann_colname <- paste0(\"pANN_0.25_\", best.pK, \"_\", nExp_poi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fbd0dfd-c7af-45f2-bdd1-93a2ea5230a3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "if (is.data.frame(MACS@meta.data[[pann_colname]])) {\n",
    "    MACS@meta.data[[pann_colname]] <- as.vector(MACS@meta.data[[pann_colname]][,1])\n",
    "  }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9e18272-5fe6-4eda-a1ef-508f633c126a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "MACS <- doubletFinder(\n",
    "MACS,\n",
    "PCs = PCs,\n",
    "pN = 0.25,\n",
    "pK = best.pK,\n",
    "nExp = nExp_poi.adj,\n",
    "reuse.pANN = pann_colname,\n",
    "sct = FALSE\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68ff41d7-3cca-48f2-b416-ce02b90d8a0d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "colnames(MACS@meta.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "187d7696-1e59-4a67-8f98-d80014bb0f67",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "doublet_var <- paste0(\"DF.classifications_0.25_\", best.pK, \"_\", nExp_poi.adj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c29217a-38a7-4e2e-93d6-f17573029d73",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "MACS[[\"Doublet_Singlet\"]] <- MACS[[doublet_var]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9896730e-07cd-4aeb-827b-136ad2acf587",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pattern <- paste0(\"0.25_\", best.pK)\n",
    "meta_names <- colnames(MACS@meta.data)\n",
    "MACS@meta.data[, grep(pattern, meta_names)] <- NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cfe7854-1297-4a3d-b5d3-fcc40ecc8945",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "FindDoublets <- function(seurat.rna, PCs = 1:50, exp_rate = 0.02, sct = FALSE) {\n",
    "  # Step 1: Parameter Sweep\n",
    "  sweep.res.list <- paramSweep(seurat.rna, PCs = PCs, sct = sct)\n",
    "  sweep.stats <- summarizeSweep(sweep.res.list, GT = FALSE)\n",
    "  bcmvn <- find.pK(sweep.stats)\n",
    "  \n",
    "  # 建议：自动选取最佳 pK（BCmetric 最大值）\n",
    "  best.pK <- as.numeric(as.character(bcmvn$pK[which.max(bcmvn$BCmetric)]))\n",
    "  \n",
    "  # Step 2: Estimate Homotypic Doublets\n",
    "  # if (!\"seurat_clusters\" %in% colnames(seurat.rna@meta.data)) {\n",
    "  #   stop(\"Missing `seurat_clusters` column in metadata. Please run clustering first (e.g. FindClusters()).\")\n",
    "  # }\n",
    "  annotations <- seurat.rna@meta.data$seurat_clusters\n",
    "  homotypic.prop <- modelHomotypic(annotations)\n",
    "  nExp_poi <- round(exp_rate * ncol(seurat.rna))\n",
    "  nExp_poi.adj <- round(nExp_poi * (1 - homotypic.prop))\n",
    "  \n",
    "  # Step 3: First pass (non-adjusted)\n",
    "  seurat.rna <- doubletFinder(\n",
    "    seurat.rna,\n",
    "    PCs = PCs,\n",
    "    pN = 0.25,\n",
    "    pK = best.pK,\n",
    "    nExp = nExp_poi,\n",
    "    # reuse.pANN = FALSE,  # 第一次FALSE\n",
    "    sct = sct\n",
    "  )\n",
    "    \n",
    "    \n",
    "  # Step 3.5: Force pANN to vector\n",
    "  pann_colname <- paste0(\"pANN_0.25_\", best.pK, \"_\", nExp_poi)\n",
    "  if (is.data.frame(seurat.rna@meta.data[[pann_colname]])) {\n",
    "    seurat.rna@meta.data[[pann_colname]] <- as.vector(seurat.rna@meta.data[[pann_colname]][,1])\n",
    "  }\n",
    "\n",
    "  # Step 4: Second pass (adjusted for homotypic)\n",
    "  seurat.rna <- doubletFinder(\n",
    "    seurat.rna,\n",
    "    PCs = PCs,\n",
    "    pN = 0.25,\n",
    "    pK = best.pK,\n",
    "    nExp = nExp_poi.adj,\n",
    "    reuse.pANN = pann_colname,\n",
    "    sct = sct\n",
    "  )\n",
    "\n",
    "  # Step 5: Final labeling\n",
    "  doublet_var <- paste0(\"DF.classifications_0.25_\", best.pK, \"_\", nExp_poi.adj)\n",
    "  seurat.rna[[\"Doublet_Singlet\"]] <- seurat.rna[[doublet_var]]\n",
    "\n",
    "  # Step 6: Optional cleanup\n",
    "  pattern <- paste0(\"0.25_\", best.pK)\n",
    "  meta_names <- colnames(seurat.rna@meta.data)\n",
    "  seurat.rna@meta.data[, grep(pattern, meta_names)] <- NULL\n",
    "\n",
    "  # Step 7: (Optional) Keep only singlets\n",
    "  seurat.rna <- subset(seurat.rna, subset = Doublet_Singlet == \"Singlet\")\n",
    "\n",
    "  return(seurat.rna)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c65fd492-c0dc-4a28-b6f8-dcfa59043179",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ob.list <- list(MACS, H21, H23,H24,H32,H33,H34,H35, H36, H38, H39, H41)\n",
    "for (i in 1:length(ob.list)) {\n",
    "  ob.list[[i]][[\"percent.mt\"]] <- PercentageFeatureSet(ob.list[[i]], pattern = \"^MT-\")\n",
    "  ob.list[[i]] <- subset(ob.list[[i]], subset = nFeature_RNA > 100 & nFeature_RNA < 10000 & percent.mt < 10)\n",
    "  # ob.list[[i]] <- subset(ob.list[[i]], cells = cells.use)\n",
    "  ob.list[[i]] <- NormalizeData(ob.list[[i]])\n",
    "  ob.list[[i]] <- FindVariableFeatures(ob.list[[i]])\n",
    "  ob.list[[i]] <- ScaleData(ob.list[[i]])\n",
    "  ob.list[[i]] <- RunPCA(ob.list[[i]], features = VariableFeatures(object = ob.list[[i]]))\n",
    "  ob.list[[i]] <- RunUMAP(ob.list[[i]], dims = 1:30, reduction.name = \"UMAP_dim30\", reduction.key = \"UMAP_dim30_\")\n",
    "  ob.list[[i]] <- RunUMAP(ob.list[[i]], dims = 1:50, reduction.name = \"UMAP_dim50\", reduction.key = \"UMAP_dim50_\")\n",
    "  ob.list[[i]] <- FindNeighbors(ob.list[[i]], dims = 1:30)\n",
    "  ob.list[[i]] <- FindClusters(ob.list[[i]], resolution = 1)\n",
    "  ob.list[[i]] <- FindDoublets(ob.list[[i]], PCs = 1:30, sct = FALSE, exp_rate = (length(colnames(ob.list[[i]]))/125000))\n",
    "}\n",
    "\n",
    "MACS <- ob.list[[1]]\n",
    "H21 <- ob.list[[2]]\n",
    "H23 <- ob.list[[3]]\n",
    "H24 <- ob.list[[4]]\n",
    "H32 <- ob.list[[5]]\n",
    "H33 <- ob.list[[6]]\n",
    "H34 <- ob.list[[7]]\n",
    "H35 <- ob.list[[8]]\n",
    "H36 <- ob.list[[9]]\n",
    "H38 <- ob.list[[10]]\n",
    "H39 <- ob.list[[11]]\n",
    "H41 <- ob.list[[12]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ea0a488-a9ac-4112-96a2-4512c81e25c6",
   "metadata": {},
   "source": [
    "## saved and loaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2bfae4e2-0c71-45cb-8b35-9fa40884f872",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ob_list <- list(MACS = MACS, H21 = H21, H23 = H23, H24 = H24, \n",
    "                H32 = H32, H33 = H33, H34 = H34, H35 = H35, \n",
    "                H36 = H36, H38 = H38, H39 = H39, H41 = H41)\n",
    "saveRDS(ob_list, file = \"../data/remove_doublets/all_objects.rds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6b9230bf-30ea-467c-b2de-510e21b11276",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ob_list <- readRDS(\"../data/remove_doublets/all_objects.rds\")\n",
    "MACS <- ob_list$MACS\n",
    "H21 <- ob_list$H21\n",
    "H23 <- ob_list$H23\n",
    "H24 <- ob_list$H24\n",
    "H32 <- ob_list$H32\n",
    "H33 <- ob_list$H33\n",
    "H34 <- ob_list$H34\n",
    "H35 <- ob_list$H35\n",
    "H36 <- ob_list$H36\n",
    "H38 <- ob_list$H38\n",
    "H39 <- ob_list$H39\n",
    "H41 <- ob_list$H41"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d4706e9e-dd46-4375-b665-5ac6c5ccc6f5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "library(DoubletFinder)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "057e2fba-e453-4b70-8d9c-9b7be053195b",
   "metadata": {},
   "source": [
    "# Merge all seurat objects together "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "629d916e-d75c-47ea-b824-61a38409b7c1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error: object 'combined' not found\n",
     "output_type": "error",
     "traceback": [
      "Error: object 'combined' not found\nTraceback:\n",
      "1. .handleSimpleError(function (cnd) \n . {\n .     watcher$capture_plot_and_output()\n .     cnd <- sanitize_call(cnd)\n .     watcher$push(cnd)\n .     switch(on_error, continue = invokeRestart(\"eval_continue\"), \n .         stop = invokeRestart(\"eval_stop\"), error = NULL)\n . }, \"object 'combined' not found\", base::quote(eval(expr, envir)))"
     ]
    }
   ],
   "source": [
    "variable_genes <- VariableFeatures(combined)\n",
    "print(paste(\"Variable genes:\", length(variable_genes)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3055e8b6-9dc4-4c78-bfef-1a6ec1796c61",
   "metadata": {},
   "source": [
    "## 第二次 过滤"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "20b241f3-615c-4d72-bdb7-734873252fe9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "27155 features across 11589 samples within 1 assay \n",
       "Active assay: RNA (27155 features, 2000 variable features)\n",
       " 3 layers present: counts, data, scale.data\n",
       " 3 dimensional reductions calculated: pca, UMAP_dim30, UMAP_dim50"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "H23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b480f965-8f7e-4b94-85f3-b1eaa4779e7b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "combined = merge(ob_list[[1]],y = ob_list[-1],add.cell.ids = c(\"H14_MACS\", \"H21\", \"H23\", \"H24\", \"H32\", \"H33\", \"H34\", \"H35\", \"H36\", \"H38\", \"H39\", \"H41\"), project = \"SB62_NBM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "79cfa2a3-51df-45e8-854a-1499a9a4ffec",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts.H14_MACS\n",
      "\n",
      "Normalizing layer: counts.H21\n",
      "\n",
      "Normalizing layer: counts.H23\n",
      "\n",
      "Normalizing layer: counts.H24\n",
      "\n",
      "Normalizing layer: counts.H32\n",
      "\n",
      "Normalizing layer: counts.H33\n",
      "\n",
      "Normalizing layer: counts.H34\n",
      "\n",
      "Normalizing layer: counts.H35\n",
      "\n",
      "Normalizing layer: counts.H36\n",
      "\n",
      "Normalizing layer: counts.H38\n",
      "\n",
      "Normalizing layer: counts.H39\n",
      "\n",
      "Normalizing layer: counts.H41\n",
      "\n",
      "Finding variable features for layer counts.H14_MACS\n",
      "\n",
      "Finding variable features for layer counts.H21\n",
      "\n",
      "Finding variable features for layer counts.H23\n",
      "\n",
      "Finding variable features for layer counts.H24\n",
      "\n",
      "Finding variable features for layer counts.H32\n",
      "\n",
      "Finding variable features for layer counts.H33\n",
      "\n",
      "Finding variable features for layer counts.H34\n",
      "\n",
      "Finding variable features for layer counts.H35\n",
      "\n",
      "Finding variable features for layer counts.H36\n",
      "\n",
      "Finding variable features for layer counts.H38\n",
      "\n",
      "Finding variable features for layer counts.H39\n",
      "\n",
      "Finding variable features for layer counts.H41\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "PC_ 1 \n",
      "Positive:  RHOH, IQGAP2, MZB1, RAB11FIP1, SEC11C, MCTP2, PLAC8, DERL3, BIRC3, AREG \n",
      "\t   KLHL6, BCL11A, SLAMF7, HMGA1, FKBP11, PLEK, TBXAS1, POU2AF1, AIF1, PIK3R5 \n",
      "\t   TMEM156, CD79A, FAM30A, JARID2, CD27, CD69, FCRL5, S100A9, HIST1H1D, SLC12A6 \n",
      "Negative:  CALD1, KAZN, LHFPL6, CDH11, RBMS3, COL1A2, COL6A2, FBN1, BICC1, NNMT \n",
      "\t   DCN, FSTL1, FBXL7, MAGI2, CHL1, NAV2, TMEM108, SELENOP, MAPK10, EBF1 \n",
      "\t   LAMA4, COL3A1, BMP5, CFH, LUM, MGP, DLC1, APOE, VCAN, GHR \n",
      "PC_ 2 \n",
      "Positive:  CHL1, TMEM108, EYA1, NCAM2, APOE, BICC1, BMP5, CFD, PAPPA, BMPER \n",
      "\t   CDH11, KAZN, LINC02384, CHRDL1, ABCA8, SLC1A3, FGF7, CP, TMEM132C, TENM4 \n",
      "\t   ROR2, DCN, TF, OLFML3, NAV2, PDZRN4, COL1A2, TNFAIP6, ADAMTS17, LUM \n",
      "Negative:  ADGRL4, LDB2, EMCN, VWF, FLT1, PTPRB, TM4SF1, CALCRL, TEK, ARHGAP29 \n",
      "\t   RAMP3, ANO2, A2M, RNASE1, PALMD, NOSTRIN, CLEC14A, RAMP2, PLVAP, GIMAP7 \n",
      "\t   IFI27, PODXL, CYYR1, GNG11, PLAT, FABP4, AQP1, DNASE1L3, BTNL9, TM4SF18 \n",
      "PC_ 3 \n",
      "Positive:  DERL3, SEC11C, FKBP11, SLAMF7, MZB1, FCRL5, TNFRSF17, CD27, DUSP5, IFNG-AS1 \n",
      "\t   IGHG1, RAB30, PRDM1, COBLL1, JSRP1, PELI1, SCNN1B, LINC00513, POU2AF1, IGHG3 \n",
      "\t   TBC1D9, IGHA1, IGLC2, RASGRP3, FAAH2, IGLV3-1, SLAMF1, JCHAIN, TMEM156, AQP3 \n",
      "Negative:  STMN1, HMGA1, TYMS, PCLAF, CDK6, GNA15, PRSS57, AC084033.3, TUBB, CENPF \n",
      "\t   MYB, DIAPH3, DUT, NUSAP1, BCL11A, TOP2A, CKS2, HELLS, HMGB2, MKI67 \n",
      "\t   HLA-DRA, AC016205.1, AIF1, ATAD2, HIST1H1B, SOX4, SMC4, CENPW, RNF220, UHRF1 \n",
      "PC_ 4 \n",
      "Positive:  MYH11, RERGL, RGS5, PRKG1, CASQ2, RCAN2, MRVI1, ACTA2, SOD3, KCNAB1 \n",
      "\t   LDB3, PHLDA2, MCAM, TPM2, NDUFA4L2, DGKB, MAP1B, PLN, CPE, LBH \n",
      "\t   PDE3A, MYL9, CNN1, CDH6, NMNAT2, CCDC3, PDGFA, LMOD1, ADIRF, COX4I2 \n",
      "Negative:  NAV3, PREX2, VCAN, P3H2, LEPR, NCAM2, EMCN, C7, CHL1, EYA1 \n",
      "\t   NAV2, THSD7A, ADGRL4, CXCL12, VCAM1, CP, SNTG2, CHRDL1, PALMD, LINC02384 \n",
      "\t   CNTNAP2, VWF, PAPPA, TMEM108, PLXNA4, SLC1A3, PTPRB, ADAM28, PDE10A, ROR2 \n",
      "PC_ 5 \n",
      "Positive:  WIF1, NCAM1, IBSP, SFRP4, MMP16, BGLAP, SRGAP3, OMD, SPP1, WDR72 \n",
      "\t   CHAD, PTPRD, S100A13, INSC, CADM1, LRP4, LMO7, PTH1R, SLC44A5, IFITM5 \n",
      "\t   COL13A1, PDGFD, CLEC11A, NRP2, RUNX2, COLEC12, BMP3, ENPP1, SLIT2, ANO5 \n",
      "Negative:  KCNAB1, MYH11, RGS5, MCAM, RERGL, CASQ2, MRVI1, NR2F2-AS1, RCAN2, EDNRA \n",
      "\t   NTRK2, RGS6, LDB3, PLN, PDE3A, LMOD1, DGKB, PHLDA2, SOX5, NRXN3 \n",
      "\t   DMD, NMNAT2, C11orf96, SLC7A2, CDH6, CRISPLD2, COX4I2, SYNPO2, ACTA2, RBPMS \n",
      "\n",
      "Warning message:\n",
      "“The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric\n",
      "To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'\n",
      "This message will be shown once per session”\n",
      "12:54:01 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "12:54:01 Read 91060 rows and found 30 numeric columns\n",
      "\n",
      "12:54:01 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "12:54:01 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
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      "[----|----|----|----|----|----|----|----|----|----|\n",
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      "\n",
      "12:54:11 Writing NN index file to temp file /tmp/RtmpUbP0Dk/file27056b4a8ce07d\n",
      "\n",
      "12:54:11 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "12:54:39 Annoy recall = 100%\n",
      "\n",
      "12:54:40 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "12:54:44 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "12:54:58 Commencing optimization for 200 epochs, with 4019162 positive edges\n",
      "\n",
      "12:54:58 Using rng type: pcg\n",
      "\n",
      "12:55:43 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "12:55:43 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "12:55:43 Read 91060 rows and found 50 numeric columns\n",
      "\n",
      "12:55:43 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "12:55:43 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
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      "\n",
      "12:55:55 Writing NN index file to temp file /tmp/RtmpUbP0Dk/file27056b3c9b7ea0\n",
      "\n",
      "12:55:55 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "12:56:22 Annoy recall = 100%\n",
      "\n",
      "12:56:24 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "12:56:29 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "12:56:42 Commencing optimization for 200 epochs, with 4018188 positive edges\n",
      "\n",
      "12:56:42 Using rng type: pcg\n",
      "\n",
      "12:57:29 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 91060\n",
      "Number of edges: 3325772\n",
      "\n",
      "Running Louvain algorithm with multilevel refinement...\n",
      "Maximum modularity in 10 random starts: 0.9374\n",
      "Number of communities: 41\n",
      "Elapsed time: 28 seconds\n"
     ]
    }
   ],
   "source": [
    "ob.list <- list(combined)\n",
    "\n",
    "for (i in 1:length(ob.list)) {\n",
    "  ob.list[[i]][[\"percent.mt\"]] <- PercentageFeatureSet(ob.list[[i]], pattern = \"^MT-\")\n",
    "  ob.list[[i]] <- subset(ob.list[[i]], subset = nFeature_RNA > 100 & nFeature_RNA < 10000 & percent.mt < 10)\n",
    "  ob.list[[i]] <- subset(ob.list[[i]], subset = Doublet_Singlet == \"Singlet\")\n",
    "  ob.list[[i]] <- NormalizeData(ob.list[[i]])\n",
    "  ob.list[[i]] <- FindVariableFeatures(ob.list[[i]])\n",
    "  ob.list[[i]] <- ScaleData(ob.list[[i]])\n",
    "  ob.list[[i]] <- RunPCA(ob.list[[i]], features = VariableFeatures(object = ob.list[[i]]))\n",
    "  ob.list[[i]] <- RunUMAP(ob.list[[i]], dims = 1:30, reduction.name = \"UMAP_dim30\", reduction.key = \"UMAP_dim30_\")\n",
    "  ob.list[[i]] <- RunUMAP(ob.list[[i]], dims = 1:50, reduction.name = \"UMAP_dim50\", reduction.key = \"UMAP_dim50_\")\n",
    "  ob.list[[i]] <- FindNeighbors(ob.list[[i]], dims = 1:30)\n",
    "  ob.list[[i]] <- FindClusters(ob.list[[i]], algorithm = 2, resolution = 1)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4931786b-de5b-4642-a367-12bfb59ac5fe",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in FindClusters.Seurat(combined, algorithm = 2, resolution = 1.5, : Provided graph.name not present in Seurat object\n",
     "output_type": "error",
     "traceback": [
      "Error in FindClusters.Seurat(combined, algorithm = 2, resolution = 1.5, : Provided graph.name not present in Seurat object\nTraceback:\n",
      "1. FindClusters.Seurat(combined, algorithm = 2, resolution = 1.5, \n .     group.singletons = T)",
      "2. stop(\"Provided graph.name not present in Seurat object\")",
      "3. .handleSimpleError(function (cnd) \n . {\n .     watcher$capture_plot_and_output()\n .     cnd <- sanitize_call(cnd)\n .     watcher$push(cnd)\n .     switch(on_error, continue = invokeRestart(\"eval_continue\"), \n .         stop = invokeRestart(\"eval_stop\"), error = NULL)\n . }, \"Provided graph.name not present in Seurat object\", base::quote(FindClusters.Seurat(combined, \n .     algorithm = 2, resolution = 1.5, group.singletons = T)))"
     ]
    }
   ],
   "source": [
    "combined <- FindClusters(combined, algorithm = 2, resolution = 1.5, group.singletons = T)\n",
    "\n",
    "combined <- ob.list[[1]] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b997380c-f5b4-4ce6-9a1d-1ace91592d3d",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 直接处理 combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "08fa6e5d-4336-48fa-addf-008c8f4cb521",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts.H14_MACS\n",
      "\n",
      "Normalizing layer: counts.H21\n",
      "\n",
      "Normalizing layer: counts.H23\n",
      "\n",
      "Normalizing layer: counts.H24\n",
      "\n",
      "Normalizing layer: counts.H32\n",
      "\n",
      "Normalizing layer: counts.H33\n",
      "\n",
      "Normalizing layer: counts.H34\n",
      "\n",
      "Normalizing layer: counts.H35\n",
      "\n",
      "Normalizing layer: counts.H36\n",
      "\n",
      "Normalizing layer: counts.H38\n",
      "\n",
      "Normalizing layer: counts.H39\n",
      "\n",
      "Normalizing layer: counts.H41\n",
      "\n",
      "Finding variable features for layer counts.H14_MACS\n",
      "\n",
      "Finding variable features for layer counts.H21\n",
      "\n",
      "Finding variable features for layer counts.H23\n",
      "\n",
      "Finding variable features for layer counts.H24\n",
      "\n",
      "Finding variable features for layer counts.H32\n",
      "\n",
      "Finding variable features for layer counts.H33\n",
      "\n",
      "Finding variable features for layer counts.H34\n",
      "\n",
      "Finding variable features for layer counts.H35\n",
      "\n",
      "Finding variable features for layer counts.H36\n",
      "\n",
      "Finding variable features for layer counts.H38\n",
      "\n",
      "Finding variable features for layer counts.H39\n",
      "\n",
      "Finding variable features for layer counts.H41\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "PC_ 1 \n",
      "Positive:  RHOH, IQGAP2, MZB1, RAB11FIP1, SEC11C, MCTP2, PLAC8, DERL3, BIRC3, AREG \n",
      "\t   KLHL6, BCL11A, SLAMF7, HMGA1, FKBP11, PLEK, TBXAS1, POU2AF1, AIF1, PIK3R5 \n",
      "\t   TMEM156, CD79A, FAM30A, JARID2, CD27, CD69, FCRL5, S100A9, HIST1H1D, SLC12A6 \n",
      "Negative:  CALD1, KAZN, LHFPL6, CDH11, RBMS3, COL1A2, COL6A2, FBN1, BICC1, NNMT \n",
      "\t   DCN, FSTL1, FBXL7, MAGI2, CHL1, NAV2, TMEM108, SELENOP, MAPK10, EBF1 \n",
      "\t   LAMA4, COL3A1, BMP5, CFH, LUM, MGP, DLC1, APOE, VCAN, GHR \n",
      "PC_ 2 \n",
      "Positive:  CHL1, TMEM108, EYA1, NCAM2, APOE, BICC1, BMP5, CFD, PAPPA, BMPER \n",
      "\t   CDH11, KAZN, LINC02384, CHRDL1, ABCA8, SLC1A3, FGF7, CP, TMEM132C, TENM4 \n",
      "\t   ROR2, DCN, TF, OLFML3, NAV2, PDZRN4, COL1A2, TNFAIP6, ADAMTS17, LUM \n",
      "Negative:  ADGRL4, LDB2, EMCN, VWF, FLT1, PTPRB, TM4SF1, CALCRL, TEK, ARHGAP29 \n",
      "\t   RAMP3, ANO2, A2M, RNASE1, PALMD, NOSTRIN, CLEC14A, RAMP2, PLVAP, GIMAP7 \n",
      "\t   IFI27, PODXL, CYYR1, GNG11, PLAT, FABP4, AQP1, DNASE1L3, BTNL9, TM4SF18 \n",
      "PC_ 3 \n",
      "Positive:  DERL3, SEC11C, FKBP11, SLAMF7, MZB1, FCRL5, TNFRSF17, CD27, DUSP5, IFNG-AS1 \n",
      "\t   IGHG1, RAB30, PRDM1, COBLL1, JSRP1, PELI1, SCNN1B, LINC00513, POU2AF1, IGHG3 \n",
      "\t   TBC1D9, IGHA1, IGLC2, RASGRP3, FAAH2, IGLV3-1, SLAMF1, JCHAIN, TMEM156, AQP3 \n",
      "Negative:  STMN1, HMGA1, TYMS, PCLAF, CDK6, GNA15, PRSS57, AC084033.3, TUBB, CENPF \n",
      "\t   MYB, DIAPH3, DUT, NUSAP1, BCL11A, TOP2A, CKS2, HELLS, HMGB2, MKI67 \n",
      "\t   HLA-DRA, AC016205.1, AIF1, ATAD2, HIST1H1B, SOX4, SMC4, CENPW, RNF220, UHRF1 \n",
      "PC_ 4 \n",
      "Positive:  MYH11, RERGL, RGS5, PRKG1, CASQ2, RCAN2, MRVI1, ACTA2, SOD3, KCNAB1 \n",
      "\t   LDB3, PHLDA2, MCAM, TPM2, NDUFA4L2, DGKB, MAP1B, PLN, CPE, LBH \n",
      "\t   PDE3A, MYL9, CNN1, CDH6, NMNAT2, CCDC3, PDGFA, LMOD1, ADIRF, COX4I2 \n",
      "Negative:  NAV3, PREX2, VCAN, P3H2, LEPR, NCAM2, EMCN, C7, CHL1, EYA1 \n",
      "\t   NAV2, THSD7A, ADGRL4, CXCL12, VCAM1, CP, SNTG2, CHRDL1, PALMD, LINC02384 \n",
      "\t   CNTNAP2, VWF, PAPPA, TMEM108, PLXNA4, SLC1A3, PTPRB, ADAM28, PDE10A, ROR2 \n",
      "PC_ 5 \n",
      "Positive:  WIF1, NCAM1, IBSP, SFRP4, MMP16, BGLAP, SRGAP3, OMD, SPP1, WDR72 \n",
      "\t   CHAD, PTPRD, S100A13, INSC, CADM1, LRP4, LMO7, PTH1R, SLC44A5, IFITM5 \n",
      "\t   COL13A1, PDGFD, CLEC11A, NRP2, RUNX2, COLEC12, BMP3, ENPP1, SLIT2, ANO5 \n",
      "Negative:  KCNAB1, MYH11, RGS5, MCAM, RERGL, CASQ2, MRVI1, NR2F2-AS1, RCAN2, EDNRA \n",
      "\t   NTRK2, RGS6, LDB3, PLN, PDE3A, LMOD1, DGKB, PHLDA2, SOX5, NRXN3 \n",
      "\t   DMD, NMNAT2, C11orf96, SLC7A2, CDH6, CRISPLD2, COX4I2, SYNPO2, ACTA2, RBPMS \n",
      "\n",
      "13:29:53 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "13:29:53 Read 91060 rows and found 30 numeric columns\n",
      "\n",
      "13:29:53 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "13:29:53 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
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      "*\n",
      "|\n",
      "\n",
      "13:30:03 Writing NN index file to temp file /tmp/RtmpUbP0Dk/file27056b19c906ab\n",
      "\n",
      "13:30:03 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "13:30:32 Annoy recall = 100%\n",
      "\n",
      "13:30:33 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "13:30:37 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "13:30:51 Commencing optimization for 200 epochs, with 4019162 positive edges\n",
      "\n",
      "13:30:51 Using rng type: pcg\n",
      "\n",
      "13:31:36 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "13:31:37 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "13:31:37 Read 91060 rows and found 50 numeric columns\n",
      "\n",
      "13:31:37 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "13:31:37 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
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      "*\n",
      "*\n",
      "|\n",
      "\n",
      "13:31:47 Writing NN index file to temp file /tmp/RtmpUbP0Dk/file27056b37ab7dc5\n",
      "\n",
      "13:31:47 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "13:32:15 Annoy recall = 100%\n",
      "\n",
      "13:32:16 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "13:32:20 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "13:32:31 Commencing optimization for 200 epochs, with 4018188 positive edges\n",
      "\n",
      "13:32:31 Using rng type: pcg\n",
      "\n",
      "13:33:16 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 91060\n",
      "Number of edges: 3325772\n",
      "\n",
      "Running Louvain algorithm with multilevel refinement...\n",
      "Maximum modularity in 10 random starts: 0.9374\n",
      "Number of communities: 41\n",
      "Elapsed time: 28 seconds\n",
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 91060\n",
      "Number of edges: 3325772\n",
      "\n",
      "Running Louvain algorithm with multilevel refinement...\n",
      "Maximum modularity in 10 random starts: 0.9214\n",
      "Number of communities: 52\n",
      "Elapsed time: 27 seconds\n"
     ]
    }
   ],
   "source": [
    "combined[[\"percent.mt\"]] <- PercentageFeatureSet(combined, pattern = \"^MT-\")\n",
    "combined <- subset(combined, subset = nFeature_RNA > 100 & nFeature_RNA < 10000 & percent.mt < 10)\n",
    "combined <- subset(combined, subset = Doublet_Singlet == \"Singlet\")\n",
    "\n",
    "combined <- NormalizeData(combined)\n",
    "combined <- FindVariableFeatures(combined)\n",
    "combined <- ScaleData(combined)\n",
    "combined <- RunPCA(combined, features = VariableFeatures(combined))\n",
    "combined <- RunUMAP(combined, dims = 1:30, reduction.name = \"UMAP_dim30\", reduction.key = \"UMAP_dim30_\")\n",
    "combined <- RunUMAP(combined, dims = 1:50, reduction.name = \"UMAP_dim50\", reduction.key = \"UMAP_dim50_\")\n",
    "combined <- FindNeighbors(combined, dims = 1:30)\n",
    "combined <- FindClusters(combined, algorithm = 2, resolution = 1)\n",
    "combined <- FindClusters(combined, algorithm = 2, resolution = 1.5, cluster.name = \"res_1.5\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "813c2341-48e0-43d4-a26e-8504a6cb15a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "saveRDS(combined, file = \"../data/remove_doublets/combined_seurat2.rds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f45e8a22-4474-4499-b65b-d1595f3d8414",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined <- readRDS(\"../data/remove_doublets/combined_seurat2.rds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "01dd1ba9-c512-4f04-8f54-7a896e6e4d49",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating cluster 0\n",
      "\n",
      "Warning message:\n",
      "“\u001b[1m\u001b[22mThe `slot` argument of `GetAssayData()` is deprecated as of SeuratObject 5.0.0.\n",
      "\u001b[36mℹ\u001b[39m Please use the `layer` argument instead.\n",
      "\u001b[36mℹ\u001b[39m The deprecated feature was likely used in the \u001b[34mSeurat\u001b[39m package.\n",
      "  Please report the issue at \u001b[3m\u001b[34m<https://github.com/satijalab/seurat/issues>\u001b[39m\u001b[23m.”\n",
      "Warning message:\n",
      "“\u001b[1m\u001b[22m`PackageCheck()` was deprecated in SeuratObject 5.0.0.\n",
      "\u001b[36mℹ\u001b[39m Please use `rlang::check_installed()` instead.\n",
      "\u001b[36mℹ\u001b[39m The deprecated feature was likely used in the \u001b[34mSeurat\u001b[39m package.\n",
      "  Please report the issue at \u001b[3m\u001b[34m<https://github.com/satijalab/seurat/issues>\u001b[39m\u001b[23m.”\n",
      "Calculating cluster 1\n",
      "\n",
      "Calculating cluster 2\n",
      "\n",
      "Calculating cluster 3\n",
      "\n",
      "Calculating cluster 4\n",
      "\n",
      "Calculating cluster 5\n",
      "\n",
      "Calculating cluster 6\n",
      "\n",
      "Calculating cluster 7\n",
      "\n",
      "Calculating cluster 8\n",
      "\n",
      "Calculating cluster 9\n",
      "\n",
      "Calculating cluster 10\n",
      "\n",
      "Calculating cluster 11\n",
      "\n",
      "Calculating cluster 12\n",
      "\n",
      "Calculating cluster 13\n",
      "\n",
      "Calculating cluster 14\n",
      "\n",
      "Calculating cluster 15\n",
      "\n",
      "Calculating cluster 16\n",
      "\n",
      "Calculating cluster 17\n",
      "\n",
      "Calculating cluster 18\n",
      "\n",
      "Calculating cluster 19\n",
      "\n",
      "Calculating cluster 20\n",
      "\n",
      "Calculating cluster 21\n",
      "\n",
      "Calculating cluster 22\n",
      "\n",
      "Calculating cluster 23\n",
      "\n",
      "Calculating cluster 24\n",
      "\n",
      "Calculating cluster 25\n",
      "\n",
      "Calculating cluster 26\n",
      "\n",
      "Calculating cluster 27\n",
      "\n",
      "Calculating cluster 28\n",
      "\n",
      "Calculating cluster 29\n",
      "\n",
      "Calculating cluster 30\n",
      "\n",
      "Calculating cluster 31\n",
      "\n",
      "Calculating cluster 32\n",
      "\n",
      "Calculating cluster 33\n",
      "\n",
      "Calculating cluster 34\n",
      "\n",
      "Calculating cluster 35\n",
      "\n",
      "Calculating cluster 36\n",
      "\n",
      "Calculating cluster 37\n",
      "\n",
      "Calculating cluster 38\n",
      "\n",
      "Calculating cluster 39\n",
      "\n",
      "Calculating cluster 40\n",
      "\n",
      "Calculating cluster 41\n",
      "\n",
      "Calculating cluster 42\n",
      "\n",
      "Calculating cluster 43\n",
      "\n",
      "Calculating cluster 44\n",
      "\n",
      "Calculating cluster 45\n",
      "\n",
      "Calculating cluster 46\n",
      "\n",
      "Calculating cluster 47\n",
      "\n",
      "Calculating cluster 48\n",
      "\n",
      "Calculating cluster 49\n",
      "\n",
      "Calculating cluster 50\n",
      "\n",
      "Calculating cluster 51\n",
      "\n"
     ]
    }
   ],
   "source": [
    "combined <- JoinLayers(combined)\n",
    "DefaultAssay(combined) <- \"RNA\"\n",
    "\n",
    "combined_markers <- FindAllMarkers(combined, max.cells.per.ident = 1000) # Downsample 1000 cells per cluster\n",
    "write.csv(combined_markers, \"../data/remove_doublets/SB66_combined_markers2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "64c2382e-bcd4-49f3-bb99-87188f06e4a8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VfcqZKt+tf9GnwHfywF6kyi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\nVLA9ZdnnmRlpj73w/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t6YK8JO2481OSXuq61/kHbSvp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+wfKH2hMrtZ3f6EMgp1pRceZ2T/CivkWZOqHGTl8s2KZHWoe+edSEPic946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Oj33nYVKoEWAXkt86v86rM47ENB7ueiAkedcsuE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\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pYOzi4VfIp2K1X6WwtLM8o+Ek+Pb9Fi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\nwKvT8epP4ul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       "plot without title"
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      "image/png": {
       "height": 960,
       "width": 960
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Idents(combined) <- combined$seurat_clusters\n",
    "options(repr.plot.width = 16, repr.plot.height = 16)\n",
    "combined <- JoinLayers(combined)\n",
    "DefaultAssay(combined) <- \"RNA\"\n",
    "# cal2_cols <- c(\"#FFD580\", \"#FFBF00\", \"#B0DFE6\", \"#7DB954\", \"#64864A\", \"#8FCACA\", \"#4682B4\", \"#CAA7DD\", \"#B6D0E2\", \"#a15891\", \"#FED7C3\", \"#A8A2D2\", \"#CF9FFF\", \"#9C58A1\", \"#2874A6\", \"#96C5D7\", \"#63BA97\", \"#BF40BF\", \"#953553\", \"#6495ED\", \"#E7C7DC\", \"#5599C8\", \"#FA8072\", \"#F3B0C3\", \"#F89880\", \"#40B5AD\", \"#019477\", \"#97C1A9\", \"#C6DBDA\", \"#CCE2CB\", \"#79127F\", \"#FFC5BF\", \"#ff9d5c\", \"#FFC8A2\", \"#DD3F4E\")\n",
    "DimPlot(combined, label = TRUE, label.size = 7, label.box = TRUE, raster.dpi = c(1028,1028),raster = TRUE)\n",
    "# pt.size = 0.5, ++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3028d15b-b8c0-4566-9eef-cf26eb4e453a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e9bdefe1-6b6e-4b63-98f9-6a8dae28c2ad",
   "metadata": {},
   "source": [
    "# Find and save all markers for combined UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91154f9a-6541-48b3-a94c-6270f5c6bb61",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "combined <- JoinLayers(combined)\n",
    "DefaultAssay(combined) <- \"RNA\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22ccff8d-590e-44fa-96dc-66f72ff622d5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "combined_markers <- FindAllMarkers(combined, max.cells.per.ident = 1000) # Downsample 1000 cells per cluster\n",
    "write.csv(combined_markers, \"../data/remove_doublets/SB66_combined_markers.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8599fdba-61bd-4789-a5b6-12f682073889",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'orig.ident'</li><li>'nCount_RNA'</li><li>'nFeature_RNA'</li><li>'percent.mt'</li><li>'RNA_snn_res.1'</li><li>'seurat_clusters'</li><li>'Doublet_Singlet'</li><li>'res_1.5'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'orig.ident'\n",
       "\\item 'nCount\\_RNA'\n",
       "\\item 'nFeature\\_RNA'\n",
       "\\item 'percent.mt'\n",
       "\\item 'RNA\\_snn\\_res.1'\n",
       "\\item 'seurat\\_clusters'\n",
       "\\item 'Doublet\\_Singlet'\n",
       "\\item 'res\\_1.5'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'orig.ident'\n",
       "2. 'nCount_RNA'\n",
       "3. 'nFeature_RNA'\n",
       "4. 'percent.mt'\n",
       "5. 'RNA_snn_res.1'\n",
       "6. 'seurat_clusters'\n",
       "7. 'Doublet_Singlet'\n",
       "8. 'res_1.5'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"orig.ident\"      \"nCount_RNA\"      \"nFeature_RNA\"    \"percent.mt\"     \n",
       "[5] \"RNA_snn_res.1\"   \"seurat_clusters\" \"Doublet_Singlet\" \"res_1.5\"        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(combined@meta.data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cda2d69a-f19e-4786-a1aa-7afeab0783c3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "Idents(combined) <- combined$seurat_clusters\n",
    "options(repr.plot.width = 20, repr.plot.height = 20)\n",
    "\n",
    "DimPlot(combined, label = TRUE, pt.size = 0.5, label.size = 7, label.box = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1166c805-99ed-4821-9cf0-49685e36aecb",
   "metadata": {},
   "source": [
    "# remove contaminating cells and doublets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "303e0525-efe2-4507-a1a9-240fd0cd0d31",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n",
      "Finding variable features for layer counts\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "Warning message:\n",
      "“Different features in new layer data than already exists for scale.data”\n",
      "PC_ 1 \n",
      "Positive:  KAZN, COL1A2, COL6A2, FBN1, FSTL1, DCN, NNMT, COL3A1, BICC1, COL6A1 \n",
      "\t   CHL1, APOE, RBMS3, CXCL12, MGP, VCAN, CYP1B1, TMEM108, NAV2, LUM \n",
      "\t   LEPR, PCOLCE, CFD, BMP5, FRMD6, NCAM2, EYA1, IGFBP4, C1R, PDE7B \n",
      "Negative:  AREG, KLHL6, TBXAS1, CD69, HIST1H1D, PLEK, TMEM156, HLA-DRA, ZNF804A, PIK3R5 \n",
      "\t   KCNQ5, STAT4, RGS1, STMN1, ATP8B4, LYZ, HLA-DQB1, PRSS57, HLA-DPA1, IRF4 \n",
      "\t   S100A9, RBM47, FLT3, PCLAF, PRDM1, S100A8, PDE3B, BMP6, MED12L, IFNG-AS1 \n",
      "PC_ 2 \n",
      "Positive:  ADGRL4, LDB2, EMCN, VWF, TM4SF1, PTPRB, FLT1, RNASE1, CALCRL, RAMP3 \n",
      "\t   RAMP2, TEK, IFI27, PALMD, GNG11, PLVAP, CLEC14A, NOSTRIN, GIMAP7, ANO2 \n",
      "\t   FABP4, DNASE1L3, AQP1, PLAT, PODXL, TM4SF18, CXorf36, PCAT19, SOX18, BTNL9 \n",
      "Negative:  CHL1, NCAM2, EYA1, TMEM108, CHRDL1, SLC1A3, BICC1, PAPPA, BMP5, CFD \n",
      "\t   BMPER, APOE, TNFAIP6, TMEM132C, CP, ROR2, ADAM28, PID1, NLGN1, FGF7 \n",
      "\t   KAZN, ANGPT1, TENM4, CNTNAP2, TF, PDZRN4, VCAN, NAV2, ADCY2, ESM1 \n",
      "PC_ 3 \n",
      "Positive:  STMN1, HLA-DRB1, HLA-DRA, TYMS, PCLAF, PRSS57, KIT, CENPF, AC016205.1, TUBB \n",
      "\t   HMGB2, DIAPH3, HLA-DPA1, TOP2A, ABCC4, XYLT1, MKI67, RNF220, NUSAP1, SMC4 \n",
      "\t   HELLS, HIST1H1B, EREG, HLA-DPB1, ATAD2, TPX2, TRIM24, NKAIN2, CMTM8, CENPU \n",
      "Negative:  CPE, MAP1B, ADIRF, SOD3, RERGL, RGS5, TPM2, MYH11, CASQ2, ACTA2 \n",
      "\t   CRYAB, NDUFA4L2, RCAN2, PHLDA2, MYL9, COL13A1, MCAM, KCNAB1, PRKG1, CADM1 \n",
      "\t   FAT1, BGN, MT1M, PLN, CRIP1, LBH, WIF1, DGKB, CNN1, NTRK3 \n",
      "PC_ 4 \n",
      "Positive:  PRKG1, STMN1, HIST1H4C, TUBB, TYMS, PCLAF, CPE, MYH11, MAP1B, SOD3 \n",
      "\t   CENPF, TPM2, TOP2A, HIST1H1B, ACTA2, ADIRF, RERGL, PCNA, RGS5, NUSAP1 \n",
      "\t   CASQ2, CRYAB, NDUFA4L2, RCAN2, LBH, MKI67, DIAPH3, CRIP1, SMC4, FAT1 \n",
      "Negative:  TBC1D9, IFNG-AS1, CARMIL1, IGHG1, PRDM1, RASGRP3, SCNN1B, JSRP1, IGHG3, TMEM156 \n",
      "\t   FAAH2, SLAMF1, IGHA1, JCHAIN, IGLV3-1, BFSP2, IGHG4, TSHZ2, ITGA6, IGLC2 \n",
      "\t   ZNF215, AC092546.1, AC007569.1, IGLC3, LY9, IGHG2, IGHGP, DNAAF1, DCC, NAV3 \n",
      "PC_ 5 \n",
      "Positive:  MYH11, KCNAB1, RGS5, RERGL, MCAM, CASQ2, RCAN2, PLN, PHLDA2, DGKB \n",
      "\t   LMOD1, ACTA2, PDE3A, CNN1, CCDC3, NR2F2-AS1, EDNRA, MYOCD, CTNNA3, RGS6 \n",
      "\t   NDUFA4L2, CPM, GJA4, CRISPLD2, C11orf96, ERBB4, AKAP6, LBH, TAGLN, SLC7A2 \n",
      "Negative:  WIF1, NCAM1, IBSP, SFRP4, MMP16, BGLAP, OMD, SRGAP3, SPP1, CHAD \n",
      "\t   PTPRD, CADM1, LRP4, INSC, RUNX2, SLC44A5, IFITM5, PDGFD, SLIT2, CLU \n",
      "\t   ENPP1, PCDH9, ANO5, TNC, DPT, FAT3, COL13A1, WISP2, PTPRZ1, RARRES1 \n",
      "\n",
      "19:20:35 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:20:35 Read 82842 rows and found 30 numeric columns\n",
      "\n",
      "19:20:35 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:20:35 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
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      "\n",
      "19:20:44 Writing NN index file to temp file /tmp/Rtmp4h2E8z/fileccb622a907d74\n",
      "\n",
      "19:20:45 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:21:11 Annoy recall = 100%\n",
      "\n",
      "19:21:12 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:21:16 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:21:30 Commencing optimization for 200 epochs, with 3681910 positive edges\n",
      "\n",
      "19:21:30 Using rng type: pcg\n",
      "\n",
      "19:22:13 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "19:22:13 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:22:13 Read 82842 rows and found 50 numeric columns\n",
      "\n",
      "19:22:13 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:22:13 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
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      "|\n",
      "\n",
      "19:22:23 Writing NN index file to temp file /tmp/Rtmp4h2E8z/fileccb624d2e5c19\n",
      "\n",
      "19:22:23 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:22:49 Annoy recall = 100%\n",
      "\n",
      "19:22:50 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:22:54 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:23:05 Commencing optimization for 200 epochs, with 3682186 positive edges\n",
      "\n",
      "19:23:05 Using rng type: pcg\n",
      "\n",
      "19:23:47 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 82842\n",
      "Number of edges: 3025398\n",
      "\n",
      "Running Louvain algorithm with multilevel refinement...\n",
      "Maximum modularity in 10 random starts: 0.9158\n",
      "Number of communities: 46\n",
      "Elapsed time: 25 seconds\n"
     ]
    }
   ],
   "source": [
    "# Cluster 46 - muscle\n",
    "# Cluster 41 - CXCL14+ Fibroblasts\n",
    "# Cluster 45 - Doublets Plasma + T\n",
    "\n",
    "# combined <- readRDS(\"./data/remove_doublets/SB66_combined.RDS\")\n",
    "combined <- subset(combined, subset = seurat_clusters != 46 & seurat_clusters != 41 & seurat_clusters != 45)\n",
    "\n",
    "ob.list <- list(combined)\n",
    "\n",
    "for (i in 1:length(ob.list)) {\n",
    "  ob.list[[i]][[\"percent.mt\"]] <- PercentageFeatureSet(ob.list[[i]], pattern = \"^MT-\")\n",
    "  ob.list[[i]] <- subset(ob.list[[i]], nFeature_RNA > 300 & nCount_RNA > 1000 & nFeature_RNA < 10000 & percent.mt < 10)\n",
    "  ob.list[[i]] <- subset(ob.list[[i]], subset = Doublet_Singlet == \"Singlet\")\n",
    "  ob.list[[i]] <- NormalizeData(ob.list[[i]])\n",
    "  ob.list[[i]] <- FindVariableFeatures(ob.list[[i]])\n",
    "  ob.list[[i]] <- ScaleData(ob.list[[i]])\n",
    "  ob.list[[i]] <- RunPCA(ob.list[[i]], features = VariableFeatures(object = ob.list[[i]]))\n",
    "  ob.list[[i]] <- RunUMAP(ob.list[[i]], dims = 1:30, reduction.name = \"UMAP_dim30\", reduction.key = \"UMAP_dim30_\")\n",
    "  ob.list[[i]] <- RunUMAP(ob.list[[i]], dims = 1:50, reduction.name = \"UMAP_dim50\", reduction.key = \"UMAP_dim50_\")\n",
    "  ob.list[[i]] <- FindNeighbors(ob.list[[i]], dims = 1:30)\n",
    "  ob.list[[i]] <- FindClusters(ob.list[[i]], algorithm = 2, resolution = 1.5)\n",
    "}\n",
    "\n",
    "combined <- ob.list[[1]] \n",
    "\n",
    "## 每一次操作后都要重新流程跑一遍"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a9c796-c8fb-4122-b7fc-b9da3d85ae1c",
   "metadata": {},
   "source": [
    "# Find and save all markers for combined UMAP\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b819d14a-aff0-4b7e-8f9b-8196d87f3073",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating cluster 0\n",
      "\n",
      "Calculating cluster 1\n",
      "\n",
      "Calculating cluster 2\n",
      "\n",
      "Calculating cluster 3\n",
      "\n",
      "Calculating cluster 4\n",
      "\n",
      "Calculating cluster 5\n",
      "\n",
      "Calculating cluster 6\n",
      "\n",
      "Calculating cluster 7\n",
      "\n",
      "Calculating cluster 8\n",
      "\n",
      "Calculating cluster 9\n",
      "\n",
      "Calculating cluster 10\n",
      "\n",
      "Calculating cluster 11\n",
      "\n",
      "Calculating cluster 12\n",
      "\n",
      "Calculating cluster 13\n",
      "\n",
      "Calculating cluster 14\n",
      "\n",
      "Calculating cluster 15\n",
      "\n",
      "Calculating cluster 16\n",
      "\n",
      "Calculating cluster 17\n",
      "\n",
      "Calculating cluster 18\n",
      "\n",
      "Calculating cluster 19\n",
      "\n",
      "Calculating cluster 20\n",
      "\n",
      "Calculating cluster 21\n",
      "\n",
      "Calculating cluster 22\n",
      "\n",
      "Calculating cluster 23\n",
      "\n",
      "Calculating cluster 24\n",
      "\n",
      "Calculating cluster 25\n",
      "\n",
      "Calculating cluster 26\n",
      "\n",
      "Calculating cluster 27\n",
      "\n",
      "Calculating cluster 28\n",
      "\n",
      "Calculating cluster 29\n",
      "\n",
      "Calculating cluster 30\n",
      "\n",
      "Calculating cluster 31\n",
      "\n",
      "Calculating cluster 32\n",
      "\n",
      "Calculating cluster 33\n",
      "\n",
      "Calculating cluster 34\n",
      "\n",
      "Calculating cluster 35\n",
      "\n",
      "Calculating cluster 36\n",
      "\n",
      "Calculating cluster 37\n",
      "\n",
      "Calculating cluster 38\n",
      "\n",
      "Calculating cluster 39\n",
      "\n",
      "Calculating cluster 40\n",
      "\n",
      "Calculating cluster 41\n",
      "\n",
      "Calculating cluster 42\n",
      "\n",
      "Calculating cluster 43\n",
      "\n",
      "Calculating cluster 44\n",
      "\n",
      "Calculating cluster 45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "combined_markers <- FindAllMarkers(combined, max.cells.per.ident = 1000) # Downsample 1000 cells per cluster\n",
    "write.csv(combined_markers, \"../data/remove_doublets/SB66_combined_markers.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "badf47b1-c0f7-48a5-b125-a1f74855a8da",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "write.csv(combined_markers, \"../data/remove_doublets/SB66_combined_markers.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1148167a-72bb-42cb-ac7e-718c59183859",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "library(VisCello)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9666bf2e-4125-4ef7-b358-3aead4f33406",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "DefaultAssay(combined) <- \"RNA\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2a0f13b4-af22-4d6a-b63b-9622de4a327a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fmeta <- data.frame(symbol = rownames(combined)) \n",
    "rownames(fmeta) <- fmeta$symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b8907a4b-b88e-4620-ab62-8564792f0816",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in assayDataNew(\"environment\", exprs = combined@assays$RNA@counts, : no slot of name \"counts\" for this object of class \"Assay5\"\n",
     "output_type": "error",
     "traceback": [
      "Error in assayDataNew(\"environment\", exprs = combined@assays$RNA@counts, : no slot of name \"counts\" for this object of class \"Assay5\"\nTraceback:\n",
      "1. initialize(value, ...)",
      "2. initialize(value, ...)",
      "3. .local(.Object, ...)",
      "4. callNextMethod(.Object, assayData = assayData, phenoData = phenoData, \n .     featureData = featureData, ...)",
      "5. eval(call, callEnv)",
      "6. eval(call, callEnv)",
      "7. .nextMethod(.Object, assayData = assayData, phenoData = phenoData, \n .     featureData = featureData, ...)",
      "8. .local(.Object, ...)",
      "9. checkClass(assayData, \"AssayData\", class(.Object))",
      "10. is(object, expected)",
      "11. assayDataNew(\"environment\", exprs = combined@assays$RNA@counts, \n  .     norm_exprs = combined@assays$RNA@data)",
      "12. .handleSimpleError(function (cnd) \n  . {\n  .     watcher$capture_plot_and_output()\n  .     cnd <- sanitize_call(cnd)\n  .     watcher$push(cnd)\n  .     switch(on_error, continue = invokeRestart(\"eval_continue\"), \n  .         stop = invokeRestart(\"eval_stop\"), error = NULL)\n  . }, \"no slot of name \\\"counts\\\" for this object of class \\\"Assay5\\\"\", \n  .     base::quote(assayDataNew(\"environment\", exprs = combined@assays$RNA@counts, \n  .         norm_exprs = combined@assays$RNA@data)))"
     ]
    }
   ],
   "source": [
    "eset <- new(\"ExpressionSet\",\n",
    "            assayData = assayDataNew(\"environment\", exprs=combined@assays$RNA@counts, \n",
    "                                     norm_exprs = combined@assays$RNA@data),\n",
    "            phenoData =  new(\"AnnotatedDataFrame\", data = combined@meta.data),\n",
    "            featureData = new(\"AnnotatedDataFrame\", data = fmeta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e9a77f50-723c-4bae-aace-4dc850d3743d",
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in assayDataNew(\"environment\", exprs = combined@assays$RNA@counts, : no slot of name \"counts\" for this object of class \"Assay5\"\n",
     "output_type": "error",
     "traceback": [
      "Error in assayDataNew(\"environment\", exprs = combined@assays$RNA@counts, : no slot of name \"counts\" for this object of class \"Assay5\"\nTraceback:\n",
      "1. initialize(value, ...)",
      "2. initialize(value, ...)",
      "3. .local(.Object, ...)",
      "4. callNextMethod(.Object, assayData = assayData, phenoData = phenoData, \n .     featureData = featureData, ...)",
      "5. eval(call, callEnv)",
      "6. eval(call, callEnv)",
      "7. .nextMethod(.Object, assayData = assayData, phenoData = phenoData, \n .     featureData = featureData, ...)",
      "8. .local(.Object, ...)",
      "9. checkClass(assayData, \"AssayData\", class(.Object))",
      "10. is(object, expected)",
      "11. assayDataNew(\"environment\", exprs = combined@assays$RNA@counts, \n  .     norm_exprs = combined@assays$RNA@data)",
      "12. .handleSimpleError(function (cnd) \n  . {\n  .     watcher$capture_plot_and_output()\n  .     cnd <- sanitize_call(cnd)\n  .     watcher$push(cnd)\n  .     switch(on_error, continue = invokeRestart(\"eval_continue\"), \n  .         stop = invokeRestart(\"eval_stop\"), error = NULL)\n  . }, \"no slot of name \\\"counts\\\" for this object of class \\\"Assay5\\\"\", \n  .     base::quote(assayDataNew(\"environment\", exprs = combined@assays$RNA@counts, \n  .         norm_exprs = combined@assays$RNA@data)))"
     ]
    }
   ],
   "source": [
    "# Annotate the cells in the final dataset ----\n",
    "\n",
    "# Prepare seurat object for viscello\n",
    "library(VisCello)\n",
    "DefaultAssay(combined) <- \"RNA\"\n",
    "fmeta <- data.frame(symbol = rownames(combined)) \n",
    "rownames(fmeta) <- fmeta$symbol\n",
    "eset <- new(\"ExpressionSet\",\n",
    "            assayData = assayDataNew(\"environment\", exprs=combined@assays$RNA@counts, \n",
    "                                     norm_exprs = combined@assays$RNA@data),\n",
    "            phenoData =  new(\"AnnotatedDataFrame\", data = combined@meta.data),\n",
    "            featureData = new(\"AnnotatedDataFrame\", data = fmeta))\n",
    "saveRDS(eset, \"../data/remove_doublets/VisCello/eset.rds\") \n",
    "# Creating a cello for all the cells\n",
    "cello <- new(\"Cello\", name = \"Combined All Cells SB66\", idx = 1:ncol(eset)) # Index is basically the column index, here all cells are included \n",
    "# Code for computing dimension reduction, not all of them is necessary, and you can input your own dimension reduction result into the cello@proj list.\n",
    "# It is also recommended that you first filter your matrix to remove low expression genes and cells, and input a matrix with variably expressed genes\n",
    "cello@proj <- list('PCA' = combined@reductions$pca@cell.embeddings,\n",
    "                   'UMAP_dim30' =combined@reductions$UMAP_dim30@cell.embeddings,\n",
    "                   'UMAP_dim50' =combined@reductions$UMAP_dim50@cell.embeddings)\n",
    "clist <- list()\n",
    "clist[[\"SB66 Global dataset\"]] <- cello\n",
    "saveRDS(clist, \"../data/remove_doublets/VisCello/clist.rds\") \n",
    "\n",
    "# Rename clusters after manual inspection of viscello obj and markers csv\n",
    "combined <- SetIdent(combined, value = \"seurat_clusters\")\n",
    "new.cluster.ids.combined <- c(\"Adipo-MSC\", \"Plasma Cell\", \"Plasma Cell\", \"Plasma Cell\", \"THY1+ MSC\", \"Osteo-MSC\",\n",
    "                              \"Neutrophil\", \"SEC\", \"Pro-B\", \"CD4+ T-Cell\", \"Osteoblast\", \"VSMC\", \"HSC\",\n",
    "                              \"Late Myeloid\", \"Late Erythroid\", \"GMP\", \"MEP\", \"OsteoFibro-MSC\",\n",
    "                              \"CD8+ T-Cell\", \"MPP\", \"Early Myeloid Progenitor\", \"GMP\", \"Fibro-MSC\", \"RNAlo MSC\", \"Monocyte\", \"Mature B\",\n",
    "                              \"AEC\", \"Cycling HSPC\", \"Pre-Pro B\", \"Late Myeloid\", \"Erythroblast\", \"Plasma Cell\", \"VSMC\",\n",
    "                              \"Pre-B\", \"pDC\", \"Ba/Eo/Ma\", \"AEC\", \"CLP\", \"Plasma Cell\",\n",
    "                              \"Cycling DCs\", \"RBC\", \"Plasma Cell\", \"MPP\", \"Macrophages\", \"RBC\", \"NKT Cell\")\n",
    "\n",
    "names(new.cluster.ids.combined) <- levels(combined)\n",
    "combined <- RenameIdents(combined, new.cluster.ids.combined)\n",
    "DimPlot(combined, label = TRUE, reduction = \"UMAP_dim30\") + coord_fixed() + NoAxes() + NoLegend()\n",
    "combined$cluster_anno_l2 <- combined@active.ident\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7e12349-d540-4851-a11c-3ed916fcb15f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mks do not cluster separately, need to subcluster erythroblasts to properly annotate\n",
    "\n",
    "# subset erythroblast cluster and rename Mks \n",
    "combined$cluster_anno_l2 <- combined@active.ident\n",
    "EBs <- subset(combined, cluster_anno_l2 == \"Erythroblast\")\n",
    "EBs <- NormalizeData(EBs)\n",
    "EBs <- FindVariableFeatures(EBs)\n",
    "EBs <- ScaleData(EBs)\n",
    "EBs <- RunPCA(EBs, reduction.name = \"EB_pca\", features = VariableFeatures(EBs))\n",
    "EBs <- RunUMAP(EBs, dims = 1:10, reduction = \"EB_pca\", reduction.name = \"EB_UMAP_dim10\")\n",
    "EBs <- RunUMAP(EBs, dims = 1:20, reduction = \"EB_pca\", reduction.name = \"EB_UMAP_dim20\")\n",
    "EBs <- RunUMAP(EBs, dims = 1:30, reduction = \"EB_pca\", reduction.name = \"EB_UMAP_dim30\")\n",
    "EBs <- RunUMAP(EBs, dims = 1:40, reduction = \"EB_pca\", reduction.name = \"EB_UMAP_dim40\")\n",
    "EBs <- RunUMAP(EBs, dims = 1:50, reduction = \"EB_pca\", reduction.name = \"EB_UMAP_dim50\")\n",
    "EBs <- FindNeighbors(EBs, dims = 1:20)\n",
    "EBs <- FindClusters(EBs, algorithm = 2, resolution = 0.5, group.singletons = FALSE)\n",
    "DimPlot(EBs, label = TRUE, reduction = \"EB_UMAP_dim50\") + coord_fixed() + NoAxes() + NoLegend()\n",
    "FeaturePlot(EBs, features = c(\"ITGA2B\", \"GATA2\"), reduction = \"EB_UMAP_dim50\", coord.fixed = TRUE) & NoAxes() \n",
    "VlnPlot(EBs, features = c(\"ITGA2B\",\"ITGB3\", \"GATA2\" ,\"GYPC\")) # can see that cluster 4 is most megakaryocytic\n",
    "# Rename Mk cluster based on CD41 expression\n",
    "Mk_ids <- colnames(subset(EBs, subset = seurat_clusters == 4)) # GATA2/CD41/CD61 expression\n",
    "combined$cluster_anno_l2 <- as.character(combined$cluster_anno_l2)\n",
    "combined$cluster_anno_l2[Mk_ids] <- \"Megakaryocyte\" \n",
    "combined$cluster_anno_l2 <- as.factor(combined$cluster_anno_l2)\n",
    "levels(combined$cluster_anno_l2)\n",
    "DimPlot(combined, label= TRUE, repel = TRUE, group.by = 'cluster_anno_l2') + NoAxes() + coord_fixed()\n",
    "\n",
    "# remove NKT cluster because they are likely doublets (T-cell markers, Osteolineage markers co-expressed)\n",
    "combined <- subset(combined, cluster_anno_l2 != \"NKT Cell\")\n",
    "\n",
    "# Make coarse annotations\n",
    "cluster_anno_coarse <- c(\"Mesenchymal\", \"Endothelial\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Mesenchymal\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Mesenchymal\", \"Mesenchymal\", \"Mesenchymal\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Mesenchymal\", \"Endothelial\", \"Mesenchymal\", \"Muscle\")\n",
    "combined <- SetIdent(combined, value = \"cluster_anno_l2\")\n",
    "names(cluster_anno_coarse) <- levels(combined)\n",
    "combined <- RenameIdents(combined, cluster_anno_coarse)\n",
    "DimPlot(combined, label = TRUE, reduction = \"UMAP_dim30\") + coord_fixed() + NoAxes() + NoLegend()\n",
    "combined$cluster_anno_coarse <- combined@active.ident\n",
    "coarse_cols <- c('#f88379','#FFA750','#6495ED','#B8242D')\n",
    "DimPlot(combined, label = TRUE, reduction = \"UMAP_dim30\", cols=coarse_cols) + coord_fixed() + NoAxes() + NoLegend()\n",
    "\n",
    "# save cluster_anno_l2 colors\n",
    "cal2_cols <- c(\"#FFD580\", \"#FFBF00\", \"#B0DFE6\", \"#7DB954\", \"#64864A\", \"#8FCACA\", \"#4682B4\", \"#CAA7DD\", \"#B6D0E2\", \"#a15891\", \"#FED7C3\", \"#A8A2D2\", \"#CF9FFF\", \"#9C58A1\", \"#2874A6\", \"#96C5D7\", \"#63BA97\", \"#BF40BF\", \"#953553\", \"#6495ED\", \"#E7C7DC\", \"#5599C8\", \"#FA8072\", \"#F3B0C3\", \"#F89880\", \"#40B5AD\", \"#019477\", \"#97C1A9\", \"#C6DBDA\", \"#CCE2CB\", \"#79127F\", \"#FFC5BF\", \"#ff9d5c\", \"#FFC8A2\", \"#DD3F4E\")\n",
    "cal2_col_names <- c(\"Adipo-MSC\", \"AEC\", \"Ba/Eo/Ma\", \"CD4+ T-Cell\", \"CD8+ T-Cell\", \"CLP\", \"Cycling DCs\", \"Cycling HSPC\", \"Early Myeloid Progenitor\", \"Erythroblast\", \"Fibro-MSC\", \"GMP\", \"HSC\", \"Late Erythroid\", \"Late Myeloid\", \"Macrophages\", \"Mature B\", \"Megakaryocyte\", \"MEP\", \"Monocyte\", \"MPP\", \"Neutrophil\", \"Osteo-MSC\", \"Osteoblast\", \"OsteoFibro-MSC\", \"pDC\", \"Plasma Cell\", \"Pre-B\", \"Pre-Pro B\", \"Pro-B\", \"RBC\", \"RNAlo MSC\", \"SEC\", \"THY1+ MSC\", \"VSMC\")\n",
    "names(cal2_cols) <- cal2_col_names\n",
    "\n",
    "combined <- SetIdent(combined, value = \"cluster_anno_l2\")\n",
    "combined_markers_anno <- FindAllMarkers(combined, max.cells.per.ident = 500) # Downsample 1000 cells per cluster\n",
    "write.csv(combined_markers_anno, \"SB66_combined_markers_annotated.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfd9c30c-088d-4174-9828-68e911bcc1cf",
   "metadata": {},
   "source": [
    "# Figure 1 Generation ----\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9505c1a5-2e3c-4efd-8649-bf01b097ade1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Figure 1 Generation ----\n",
    "\n",
    "# in text QC metrics\n",
    "combined@meta.data %>% dplyr::summarise(c(mean(nCount_RNA), mean(nFeature_RNA), mean(percent.mt)))\n",
    "\n",
    "\n",
    "# Supplemental Figure S1C QC Metrics -----\n",
    "VlnPlot(combined, group.by =\"cluster_anno_coarse\", features = c(\"nFeature_RNA\", \"nCount_RNA\", \"percent.mt\"), pt.size=0) & scale_fill_manual(values =c(\"#AEC7E8\", \"#FFBB78\", \"#98DF8A\", \"#FF9896\"))\n",
    "VlnPlot(combined, group.by =\"cluster_anno_coarse\", features = c(\"nCount_RNA\"), pt.size=0, y.max = quantile(combined$nCount_RNA,0.99)) & scale_fill_manual(values =c(\"#AEC7E8\", \"#FFBB78\", \"#98DF8A\", \"#FF9896\"))\n",
    "\n",
    "combined@meta.data %>% dplyr::group_by(cluster_anno_coarse)  %>% summarise(median(nFeature_RNA), median(nCount_RNA), median(percent.mt)) %>% gt() -> supp_fig_1D\n",
    "gtsave(supp_fig_1D, filename = \"~/Documents/Manuscripts/NBM_Atlas/Figures/Supplemental_Figures/Related_to_Figure1/Supp_Fig_1D.pdf\")\n",
    "\n",
    "# Supplemental Figure S1D UMAP Feature Plots -----\n",
    "p1 <- FeaturePlot(object = combined, raster = TRUE, raster.dpi = c(1028,1028), features = c(\"CXCL12\",\"NCAM1\",\"CDH5\", \"PTPRC\", \"MZB1\", \"CSF3R\"), cols = brewer.pal(n = 100, name = \"Reds\"),ncol = 6, max.cutoff = 'q99', coord.fixed = TRUE) & NoAxes() \n",
    "p1_raster <- rasterize(p1)\n",
    "ggsave(p1_raster,file = \"~/Documents/Manuscripts/NBM_Atlas/Figures/Supplemental_Figures/Related_to_Figure1/PanelE_scRNA_MarkerFeaturePlots.pdf\", device = \"pdf\", width = 15, height = 2)\n",
    "\n",
    "\n",
    "# Figure S1E CODEX Cell Type Frequencies Per Sample By Age ----\n",
    "ggplot(data=percentage_data, aes(x=Age,y=percentage, fill=cluster_anno_l1)) +\n",
    "  geom_bar(position=\"fill\",stat=\"identity\") + \n",
    "  theme_bw() + \n",
    "  labs(y='Cell Type Frequency') + \n",
    "  labs(x='') + scale_fill_manual(values=cal1_cols) + theme(axis.text = element_text(size=16)) + ggtitle(\"CODEX Cell Type Frequencies Per Sample\") \n",
    "\n",
    "# perform linear regression testing\n",
    "linear_regression_results <- percentage_data %>%\n",
    "  group_by(cluster_anno_l1) %>%\n",
    "  do(model = lm(percentage ~ Age, data = .))\n",
    "\n",
    "# Extract p-values and R-squared values\n",
    "result_df <- linear_regression_results %>%\n",
    "  summarise(\n",
    "    Cell_Type = cluster_anno_l1,\n",
    "    P_Value = coef(summary(model))[, \"Pr(>|t|)\"][2],\n",
    "    R_Value = summary(model)$r.squared\n",
    "  )\n",
    "\n",
    "# confirm everything adds up to 100 per patient\n",
    "percentage_data %>% group_by(orig.ident) %>% summarise(sum(percentage))\n",
    "\n",
    "# repeat for MSCs\n",
    "\n",
    "# plot cell types as function of age\n",
    "percentage_data <- MSCs@meta.data %>%\n",
    "  group_by(orig.ident, cluster_anno_l2) %>%\n",
    "  summarise(count = n()) %>% \n",
    "  mutate(percentage = count / sum(count) * 100)\n",
    "\n",
    "percentage_data <- left_join(percentage_data, additional_md, by = \"orig.ident\")\n",
    "\n",
    "# Create the ggplot2 plot\n",
    "ggplot(percentage_data, aes(x = Age, y = percentage, color = cluster_anno_l2)) +\n",
    "  geom_line() + geom_point() +\n",
    "  labs(title = \"Cell Type Percentages by Age\",\n",
    "       x = \"Age\",\n",
    "       y = \"Percentage\") +\n",
    "  scale_fill_brewer(palette = \"Set1\") +  # Choose a color palette\n",
    "  theme_minimal()\n",
    "\n",
    "linear_regression_results <- percentage_data %>%\n",
    "  group_by(cluster_anno_l2) %>%\n",
    "  do(model = lm(percentage ~ Age, data = .))\n",
    "\n",
    "# Extract p-values and R-squared values\n",
    "result_df <- linear_regression_results %>%\n",
    "  summarise(\n",
    "    Cell_Type = cluster_anno_l2,\n",
    "    P_Value = coef(summary(model))[, \"Pr(>|t|)\"][2],\n",
    "    R_Value = summary(model)$r.squared\n",
    "  )\n",
    "print(result_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d6aeb58-3e8c-41c8-bad5-c87a77120efe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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